SLAM (Simultaneous Localization and Mapping) is a robust method for tracking an object over time and mapping out its surrounding environment. It uses elements of probability, motion models, and linear algebra.
In this project, we implement SLAM for a 2 dimensional world.
We implement knowledge about robot sensor measurements and movement to create a map of an environment from only sensor and motion data gathered by a robot, over time. SLAM gives us a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, and other world features. This is an active area of research in the fields of robotics and autonomous systems.
Notebook 1 : Robot Moving and Sensing
Notebook 2 : Omega and Xi, Constraints
Notebook 3 : Landmark Detection and Tracking